The present invention relates to cancellation of noise signals from a detected signal and more particularly relates to cancellation of unknown noise using a plurality of reference sensors.
The problem of obtaining a true representation of a source signal occurring in a noisy environment occurs in a variety of circumstances, for example in the detection of sonar or seismographic signals and in the detection of a fetal electrocardiogram in the presence of maternal "noise" signals. It is known in the art that unwanted noise detected by a primary sensor can be mitigated by the use of so-called "signal-free" reference sensors, which receive only the noise and not the signal. The output of the signal-free reference sensors is used in conjunction with the output of the primary sensor to derive filter constants which are used to remove the noise components from the output of the primary sensor. It has been recognized that it is advantageous to use more than a single reference sensor when there is more than a single independent source of noise to be cancelled. It has also been recognized, however, that there is a limit on the number of reference sensors that can be used. It is a problem in the art that there is no known method for ascertaining the precise number of reference sensors that will provide cancellation of all significant noise components of the primary sensor output without compromising the integrity of the signal to be detected. Furthermore, under most circumstances it is not possible to obtain a reference sensor reading which is totally free of the primary signal. Known noise-cancelling techniques have a tendency to amplify the effects of small amounts of a primary signal which are detected by the reference sensors to the point of cancelling the signal to be detected.
One prior art publication by Widrow et al. entitled "Adaptive Noise Cancelling: Principles and Applications" proceedings of the IEEE, Volume 63, No. 12, December 1975, describes an arrangement for cancelling noise by the use of a primary sensor and one or more signal-free reference sensors in the generation of a fetal electrocardiogram. Each reference sensor output is used, together with primary sensor information, for determining constants for the so-called Wiener filter. The Wiener filter is used to filter the references prior to subtracting the filtered reference outputs from the primary output in order to generate a representation of the true signal without the undesired components. Widrow et al. use the Wiener filter on each of the references separately in an attempt to remove from the primary sensor output signal components identified from the outputs of the signal-free reference sensors. Widrow et al. indicate that it is not clear how many of the signal-free reference sensors should be used. One problem in the art is that if too few references are used, not all significant noise components will be removed from the signal. A specific problem of the prior approaches is that if more references are used than there are independent noise sources, the Wiener filter algorithm receives too much mutually coherent reference sensor information, and matrices used in the computation of the filter constants become ill-conditioned or rank deficient. This results in poorly defined filter constants or causes the computation to break down completely. Another problem with this prior art approach is that in practice, the so-called signal-free references may in fact be influenced by the primary signal to be detected. The effect of even a small amount of such a signal used in the Wiener filter process may cause the true signal to be cancelled along with the noise, since the purpose of that filter is to remove from the signal all components detected by the reference sensors. The need for a fetal electrocardiogram has been long recognized by doctors, for example, for proper diagnosis of fetal arrhythmia or other fetal cardiac abnormalities. However, no reliable device for the generation of fetal electrocardiograms has been developed in the prior art.
Another prior art publication by Cao Changxiu entitled "A New Algorithm for Adaptive Noise Cancellation Using Singular Value Decomposition" Acta Automatica Sinica Volume 12, No. 2, April 1986, describes a noise-cancelling method using a single reference sensor. The output of this sensor is passed through one or more time delays to make it available over varying time intervals. The values so obtained are configured in a matrix and singular value decomposition is proposed to reduce the matrix, treating certain values as zero. This publication deals with statistical dependence or independence of a single sensor output when examined over several time intervals. It does not address the problem of excessive mutually coherent information coming from several reference sensors used to detect a plurality of noise sources.
Other prior art references such as a publication by van Oosterom et al. entitled "Removing the Maternal Component in the Fetal ECG Using Singular Value Decomposition" Electrocardiology '83: Proceedings of the 10th International Congress on Electrocardiology, Bratislava, Czechoslovakia, Aug. 14, 1983, are concerned with the application of singular value decomposition to noise cancelling in a multi-reference environment. That publication proposes applying the singular value decomposition technique to a matrix of computed values derived both from the primary sensor output and outputs from a plurality of references. One problem with this prior art approach is that it is difficult to predict which of the values resulting from the singular value decomposition are derived from the primary source and which are derived from the reference sources. No precise way of separating the primary signal contribution from the reference signal contributions is proposed in the publication.
Another prior art publication, authored by Longini et al. entitled "Near-Orthogonal Basis Functions: A Real Time Fetal ECG Technique", IEEE Transactions on Biomedical Engineering, Volume BME-24, No. 1, January 1977, discusses the application of orthogonal basis functions to a reference matrix of values derived from signals from a plurality of reference sources. One problem with this prior art approach is that it does not deal with the problems resulting from the inclusion of small amounts of the primary signal in the reference signals or excessive mutually coherent information from several reference sensors.